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Ana Tsalitsatun Ni'mah
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INDONESIA
Jurnal Ilmiah Edutic : Pendidikan dan Informatika
Published by Universitas Trunojoyo Madura
ISSN : 24074489     EISSN : 25287303     DOI : https://doi.org/10.21107/edutic
Core Subject : Science, Education,
Computer Science & IT Education
Jurnal Ilmiah Edutic Pendidikan dan Informatika is a journal published by the Informatics Education Study Program, Universitas Trunojoyo Madura. Eductic contains publications on the results of thoughts and research in the field of education and information technology. Eductic is published twice a year, namely in May and November. FOCUS & SCOPE Edutic scientific journals focus, but are not limited to, articles within the scope of Education and Informatics. Therefore Edutic will only process and publish articles submitted in the areas of: Informatics Education (E-Learning, Multimedia Education, Vocational Learning Media, Vocational Education Psychology, Vocational Learning Strategies, Vocational Learning Theory, Vocational Learning Models, Vocational Learning Methods, Vocational Teaching Approaches, Vocational Learning Evaluation, Vocational Learning Planning). Informatics (Information Systems, Data Mining, Computer Networks, Database Systems, Electronics, Image Processing, Gaming Technology, Artificial Intelligence, Information Retrieval Systems)
Arjuna Subject : Ilmu Sosial - Pendidikan
Articles 198 Documents
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Adaptive Cognitive Learning in Vocational Education: Integrating Deep Learning for Fiber optic Network Competencies Sa'adah, Nurul Laili; Putra, Muhammad Trio Maulana; Anggana, Syeh Umar; Masykuri, Nuri Muhammadin
EDUTIC Vol 12, No 2: 2025
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/edutic.v12i2.31843

Abstract

The rapid advancement of digital technologies, particularly Deep Learning, presents opportunities to enhance adaptive cognitive learning in vocational education. This study investigates the integration of Deep Learning into the teaching of fiber optic network competencies in the Computer and Network Engineering (TJKT) program at vocational schools. Using a Systematic Literature Review (SLR) based on the PRISMA protocol, relevant studies published between 2020 and 2025 were retrieved from Scopus, IEEE Xplore, and Google Scholar. Findings reveal that Deep Learning enables real-time modeling of student learning patterns, supports personalized content delivery via learning management systems, and facilitates simulation and troubleshooting in fiber optic training. However, challenges include limited training data, inadequate computing infrastructure, and insufficient teacher readiness. The study concludes that implementing Deep Learning can significantly improve practical learning effectiveness, provided that infrastructure and educator competencies are strengthened
Development Of An Interactive E-Module Based On The Independent Curriculum In Informatics Learning At Vocational Schools Stefany, Evy Maya; Ningsih, Puji Rahayu; Diana, Luluk Mauli; Hariri, Naila
EDUTIC Vol 12, No 2: 2025
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/edutic.v12i2.32125

Abstract

This research aims to develop an interactive e-module based on the Independent Curriculum to support Informatics learning in class X, especially in Data Analysis materials. This research is motivated by learning problems at SMKN 1 Kwanyar, where the learning process still depends on PowerPoint media and structured digital learning resources are not available. The research method used is Research and Development (RD) with a 4D development model (Define, Design, Develop, and Disseminate). The e-module product was validated by two experts, namely media experts and material experts, and tested on students through two stages of field tests in the form of a small group trial involving 5 students and a large group trial involving 24 students. The validation results showed an excellent level of eligibility, namely 96.67% of media experts, 92% of material experts, and an average of 91% of student responses. The findings show that the e-modules developed are valid, practical, and effective in using digital learning media. Interactive features, systematic material structure, and web-based accessibility make this e-module suitable to support independent learning and increase student engagement according to the demands of the Independent Curriculum.
Population Growth Rate Prediction Using the Support Vector Regression Method Pratama, Rendy yudha; Andika, Tahta Herdian; Kurnia, Ulfa Isni
EDUTIC Vol 12, No 2: 2025
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/edutic.v12i2.31817

Abstract

Laju pertumbuhan penduduk yang tinggi dan terus meningkat di Indonesia merupakan isu penting karena berdampak pada berbagai sektor termasuk ekonomi, sosial, politik, dan pertahanan negara. Akibatnya, entitas terkait seperti Departemen Sosial dan Badan Keluarga Berencana Nasional (BKKBN) menganalisis faktor-faktor yang terkait dengan laju pertumbuhan penduduk untuk merumuskan kebijakan yang bertujuan mencapai pertumbuhan penduduk yang seimbang. Selain itu, prediksi pertumbuhan masyarakat dimanfaatkan oleh Dinas Kependudukan dan Pencatatan Sipil (Dispendukcapil) untuk perencanaan anggaran dan kebutuhan lainnya. Penelitian ini fokus pada prediksi laju pertumbuhan penduduk menggunakan metode Support Vector Regression (SVR) dan membandingkan kinerja kernel RBF linier dan Gaussian. Studi ini menggunakan dataset deret waktu angka populasi dari Maret 2017 hingga Desember 2022. Proses prediksi melibatkan normalisasi data, pelatihan SVR untuk mendapatkan nilai pengali Lagrange yang diperbarui, dan pengujian SVR untuk menghasilkan hasil prediksi dan tingkat kesalahan menggunakan Mean Absolute Percentage Error (MAPE). Hasil pengujian menunjukkan nilai MAPE sebesar 0,0985% untuk kernel linear dan 0,38192% untuk kernel Gaussian RBF.
Development of a Web-Based Research and Community Service Management System Using ISO 25010 Quality Standards Efendi, Rizal; Aini, Nuru; Wijaya, Etistika Yuni; Maghfirli, Fadli
EDUTIC Vol 12, No 2: 2025
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/edutic.v12i2.31932

Abstract

Research and community service data management is a vital component of higher education accreditation and strategic planning. However, the Informatics Education Study Program currently relies on manual documentation, resulting in data fragmentation and difficulties in mapping lecturers' research roadmaps. This study aims to develop a research management information system to digitize and integrate these processes, and to evaluate its technical feasibility. Using the Research and Development (RD) method with the Waterfall development model, software quality was tested based on the ISO 25010 standard, focusing on functional suitability, performance efficiency, and usability. The test results showed that the system had 100% functional suitability, a usability level of 92% (Very Good category), and performance efficiency with an average load time of 0.9 seconds. These findings confirm that the proposed system not only addresses the limitations of manual archiving but also significantly improves the effectiveness of academic data governance. This study contributes to providing a validated information system framework for intellectual asset management at the study program level.
Optimizing UKT Prediction Based on Socio-Economic Features: A Multimodel Evaluation with Feature Selection Srategies Putri, Windy Chikita Cornia; Yustanti, Wiyli; Yohannes, Ervin
EDUTIC Vol 12, No 2: 2025
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/edutic.v12i2.31828

Abstract

Determining the tuition fee group (UKT) for new students in Indonesian public universities represents a complex challenge requiring an equitable, data-driven approach. This study introduces an integrative feature selection strategy that combines five popular techniques Chi-Square, Recursive Feature Elimination (RFE), LASSO Regression, Random Forest Importance, and Exploratory Factor Analysis (EFA) to extract the most relevant attributes from 53 socioeconomic variables of prospective students at Universitas Negeri Surabaya. As a novelty, the study identifies intersecting features consistently selected by all five methods and evaluates their impact on the performance of five classification algorithms: Support Vector Machine (SVM), Decision Tree, Random Forest, K-Nearest Neighbor (KNN), and Naïve Bayes. Experimental results demonstrate a significant improvement in accuracy, with SVM increasing from 0.7550 to 0.7810. These findings confirm that integrative feature selection can optimize model performance while reducing data complexity. This study provides a replicable methodological contribution for developing transparent and adaptive classification systems based on socioeconomic data in higher education contexts.
Development of a Web-Based Online Judge for Java Programming Examinations to Enhance Interactive Learning Environments Putra, Muhammad Trio Maulana; Sa’adah, Nurul Laili; Kurnia, Nicky Dwi; Ani, Febi Warta Nur
EDUTIC Vol 12, No 2: 2025
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/edutic.v12i2.31861

Abstract

This paper addresses the challenges in traditional programming assessment, such as scalability and delayed feedback. We present the development of a web-based examination platform for the Java programming language. The system is designed with a decoupled architecture, integrating the CodeMirror editor to create an interactive learning environment and leveraging the Glot.io API for secure, sandboxed code execution. This approach mitigates security risks associated with evaluating untrusted code and provides students with real-time, automated feedback. The resulting platform offers a robust, secure, and pedagogically enhanced solution for online programming assessment, improving both the efficiency for instructors and the learning experience for students.
Implementation of ResNet50 Based on Transfer Learning for Sugarcane Leaf Disease Detection Ulum, M. Miftah Fatkhul; Sholihin, Miftahus; Mustain, Mustain
EDUTIC Vol 12, No 2: 2025
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/edutic.v12i2.31873

Abstract

Sugarcane (Saccharum officinarum) is a vital commodity in Indonesia’s sugar industry and is highly susceptible to leaf diseases such as Mosaic, RedRot, Rust, and Yellow, which significantly reduce yield quality and quantity. This study proposes an automatic disease classification system using the ResNet50 architecture with a transfer learning approach, offering a more systematic evaluation compared to previous studies that typically tested only a single configuration or focused on other crops. The dataset consists of 3,250 RGB images across five classes after preprocessing and augmentation to address class imbalance. Eight model configurations were evaluated by combining epoch values (20, 40) and learning rates (0.0001, 0.001, 0.01, 0.1). The best performance was achieved by the configuration with 20 epochs and a learning rate of 0.0001, producing an accuracy and F1-score of 97%. The model was further deployed into a Flask-based web application to demonstrate practical usability. However, this study is limited by the use of a single controlled dataset, so model performance may vary under real-field conditions such as different lighting, camera angles, and leaf damage severity. Future research should include field data evaluation to strengthen model generalization.
The Effect of Problem-Based Learning Model Assisted by Scratch in Grade VII Informatics Learning on Critical Thinking Skill Afifudin, Muhammad Abdu; Rusijono, Rusijono; Susarno, Lamijan Hadi; Rokhman, Ayla Yuli
EDUTIC Vol 12, No 2: 2025
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/edutic.v12i2.31294

Abstract

This study aims to determine the effect of the Scratch-assisted Problem-Based Learning (PBL) model on the critical thinking skills of seventh-grade students in Informatics subject, specifically on computational artifact development. The research employed a quantitative approach with a quasi-experimental design, using a pretest-posttest control group. The research sample consisted of two classes: an experimental class that implemented the Scratch-assisted PBL model, and a control class that used a conventional learning model. The critical thinking assessment instrument was developed based on five indicators from Facione: interpretation, analysis, evaluation, inference, and explanation. Data were analyzed using an independent samples t-test to determine the significance of the differences between the groups. The results of the independent samples t-test showed a statistically significant difference between the experimental and control groups (t(38) = 3.45, p = 0.004). The mean posttest score of the experimental group increased significantly from 62.4 to 83.6, with a large effect size (Cohen's d) of 1.24. The research sample consisted of 40 seventh-grade students divided into two groups. The inference and explanation indicators showed the highest improvement. In addition to the score improvement, project-based learning with Scratch also facilitated 21st-century skills such as collaboration, problem-solving, and student digital agency. The integration of Scratch provided space for computational thinking, solution visualization, and self-reflection. This study recommends the use of digital-based PBL models as an innovative learning strategy aligned with the Merdeka Curriculum.

Page 20 of 20 | Total Record : 198

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